Digital cameras and the associated digital imagery continue to replace film photography in opthalmology for archiving a patient's medical history and for preserving visual data in research of disease. Quantitative approaches for documenting the state of disease in longitudinal analysis and diagnosis can best be implemented using digital images. Unfortunately, the typical digital camera technology employed by the majority of clinics today has low resolution, low dynamic range, and otherwise poor performance with respect to retinal imaging. This has opened the market for high-priced solutions with multi-mega pixel format cameras employing 12-bit (or greater) color depth offering some improvement. Unfortunately these cameras are priced at $25,000 or more and are beyond the means of many eye clinics. The seriousness of the retinal image quality problem is reflected in discussions by renowned individuals in the fields of retinal reading and retinal disease epidemiology. These experts are acutely aware of the issues of current digital image quality. Experts in the field depend almost entirely on retinal images for their research, and would benefit from an enhanced entropy method and apparatus.
Referring now to FIG. 1, an interdependence of resolution and contrast for human perception is illustrated. The inverse contrast sensitivity, IC, is portrayed and corresponds to the formula shown, wherein Iobj is the intensity of the object of interest, and IBack is the intensity of the image background. As the spatial features become smaller (higher spatial frequency), the contrast threshold (shaded area) for the ability of a human to detect the feature increases. In other words, the smaller the feature, the higher the contrast must be between it and the surrounding background. For applications like screening, where the lesions, such as micro aneurysms, may be quite small, it becomes valuable to have a retinal image with sufficient contrast so that early signs of diabetic retinopathy, which are usually smaller and more subtle than in later stages of the disease, may be correctly identified and diagnosed.
A common technique for improving contrast is to apply equalization (contrast stretching). Referring now to FIG. 2A an image scanned using standard settings for Nikon 35 mm slide scanner is illustrated. FIG. 2B illustrates the histogram presentation of the image in FIG. 2B. Referring now to FIG. 3A, an equalized version of the digitized image in FIG. 2A is illustrated. FIG. 3B illustrates the histogram representation of the equalized image in FIG. 3A. The increased contrast may be more appealing to the human observer and may even give the human analyst added visual cues when trying to detect certain lesions. Additionally, this processing of the image will improve the performance of some segmentation algorithms. However, for the inherent differences between tissue color and brightness that fall below the threshold allowed by the digitization scheme, these differences cannot be represented in the digital image. Furthermore, standard histogram equalization does not improve the ability to detect features not captured by the initial digitization. Histogram equalization merely takes the information in the digital image and re-maps it so the human eye can better visualize it.
Histogram equalization does not add information. For example, a typical color image is digitized using 24-bits. That is, each color channel (red, green, and blue) is digitized using 8-bits. 8-bits (28) equates to 256 gray levels of brightness or intensity for each color channel. This means that any difference in intensity less than 1/256th of the dynamic range of the digitizer cannot be resolved. The dynamic range of the system is the maximum minus the minimum measurable flux of the sensing and electronics of the instrument.
In many applications where contrast may be low or where it has large variations, which is the case in retinal imaging, one can take advantage of certain features in the intensity histogram to maximize the use of the 256 gray levels allowed by the 8-bit digital representation. The histogram in FIG. 2B illustrates the point that many of the image pixels have intensities that lie in a very narrow range resulting in a low contrast. One implication of these data is that the gray levels are wasted in that they are not storing any information about the intensity levels in the image.
FIG. 3B illustrates the histograms for the image equalized. The histogram equalization has simply spread the gray levels so that the eye can better perceive them, but has not changed the fact that many of the gray levels are still not storing any information about the image and that the subtle scene intensity variations corresponding to these missing gray levels were not captured by the imager.
Several reports have proposed that a 4K×4K pixel color camera is necessary to achieve image quality found in 35-mm color slides. Human perception involves both the effects of resolution and contrast on lesion detection. By increasing contrast of lower resolution digital images, one can achieve results comparable to those of the traditional large-format cameras at a fraction of the cost. A 4K×4K color camera is very expensive. Integrating an enhanced entropy image quantizer with a 2K×2K or 1K×1K camera, such as the Spot Color RT ($13,000), would significantly reduce the cost and make digital imaging cost competitive with film photography without loss of sensitivity or specificity for screening of subtle retinal lesions. It is noted that the “K” quantity as used in this application is not intended to mean a factor of 103 per se, but is meant to be approximately that amount so as to include within its meaning at least the computer science meaning of K=1024.
For many applications in opthalmology, including general-purpose clinical screening and telemedicine, fundus images must be digitized for transmission. For purposes of screening and diagnosis, the digital imager should be of sufficiently high quality (spatial resolution and contrast) to perform specific functions in diagnosing retinal diseases. Information will be lost in the digitizing process due to spatial sampling and intensity level quantization. Quantization is the bracketing of ranges of an analog signal to correspond to a particular digital number. The lost spatial information limits the spatial resolution, while the lost intensity information results in reduced contrast. The information lost through this quantization can never be recovered by processing. Automatic non-uniform gain control techniques have been proposed which will minimize the loss. However, none of these cameras attempt to improve the exposure where the scene varies as much and has such a highly unbalanced color (hue and saturation) as a blood-perfused organ, for example, the retina. Video or photographic images that are never digitized, but are presented for human viewing, suffer a similar loss. The human vision system can perceive about 60 to 100 distinct gray levels under typical viewing conditions. It is desirable that the digital image contain at least this amount of unique information. Ophthalmic imaging can be well-served by a system that can adjust the analog signal to maximize the information content of the digital image. For clinical applications, this adjustment should be automatic, and should adapt smoothly and robustly to changing retinal illumination and naturally occurring variations in retinal pigmentation among individuals.
As with any new medical imaging modality, changes in sensitivity and specificity for diagnosing disease may result. An enhanced entropy image method for obtaining higher contrast images would provide greater visual insight. A reasonable degree of agreement should occur when film-based images and entropy enhanced digital images are compared. There is a need for high quality digital imaging to supplement or perhaps eventually replace 35-mm photographs.
To meet this challenge an enhanced entropy camera is needed that takes advantage of low cost technologies (such as 8-bit digital cameras), but implements improvements through smarter use of the available quantizer to better sample the light reflected from the subject.